February 13th, 2018, 8:27am by Sam Wang

Mathematicians have been working hard to create ways to measure #Gerrymandering. 2017 was a big year on this: accumulating evidence showed PA most gerrymandered of all by two measures, among five worst by any measure. We all lose our voice when our votes don't count. pic.twitter.com/5fgCxTc00r

Today, Philly.com highlights multiple measures of partisan gerrymandering, including several developed here at Princeton.

Now, a major disclaimer: I didn’t think of these, exactly. Several tests (mean-median difference, and lopsided-wins) are over a hundred years old. Another (simulated elections) relies on an equally-old technique, Monte Carlo simulations. These tests are so old that they have whiskers.

There’s one lesson, though: there isn’t just one way to evaluate a gerrymander. Think of these as tools that capture different aspects of partisan asymmetry. For example, Monte Carlo simulations actually account for some of the clustering effect that comes from Republicans gravitating toward rural areas and Democrats gravitating toward population centers.